TARGETING THE 16S RRNA GENE FOR BACTERIAL IDENTIFICATION IN COMPLEX MIXED SAMPLES: COMPARATIVE EVALUATION OF SECOND (ILLUMINA) AND THIRD (OXFORD NANOPORE TECHNOLOGIES) GENERATION SEQUENCING TECHNOLOGIES

Rapid, accurate bacterial identification in biological samples is an important task for microbiology laboratories, for which 16S~rRNA gene Sanger sequencing of cultured isolates is frequently used. In contrast, next-generation sequencing does not require intermediate culturing steps and can be directly applied on communities, but its performance has not been extensively evaluated. We present a comparative evaluation of second (Illumina) and third (Oxford Nanopore Technologies (ONT)) generation sequencing technologies for 16S targeted genomics using a well-characterized reference sample. Different 16S gene regions were amplified and sequenced using the Illumina MiSeq, and analyzed with Mothur. Correct classification was variable, depending on the region amplified. Using a majority vote over all regions, most false positives could be eliminated at the genus level but not the species level. Alternatively, the entire 16S gene was amplified and sequenced using the ONT MinION, and analyzed with Mothur, EPI2ME, and GraphMap. Although >99\% of reads were correctly classified at the genus level, up to $\approx$40\% were misclassified at the species level. Both~technologies, therefore, allow reliable identification of bacterial genera, but can potentially misguide identification of bacterial species, and constitute viable alternatives to Sanger sequencing for rapid analysis of mixed samples without requiring any culturing steps.

The major secreted protein Msp1/p75 is O-glycosylated in Lactobacillus rhamnosus GG

BACKGROUND

Although the occurrence, biosynthesis and possible functions of glycoproteins are increasingly documented for pathogens, glycoproteins are not yet widely described in probiotic bacteria. Nevertheless, knowledge of protein glycosylation holds important potential for better understanding specific glycan-mediated interactions of probiotics and for glycoengineering in food-grade microbes.

RESULTS

Here, we provide evidence that the major secreted protein Msp1/p75 of the probiotic Lactobacillus rhamnosus GG is glycosylated. Msp1 was shown to stain positive with periodic-acid Schiff staining, to be susceptible to chemical deglycosylation, and to bind with the mannose-specific Concanavalin A (ConA) lectin. Recombinant expression in Escherichia coli resulted in a significant reduction in molecular mass, loss of ConA reactivity and increased sensitivity towards pronase E and proteinase K. Mass spectrometry showed that Msp1 is O-glycosylated and identified a glycopeptide TVETPSSA (amino acids 101-108) bearing hexoses presumably linked to the serine residues. Interestingly, these serine residues are not present in the homologous protein of several Lactobacillus casei strains tested, which also did not bind to ConA. The role of the glycan substitutions in known functions of Msp1 was also investigated. Glycosylation did not seem to impact significantly on the peptidoglycan hydrolase activity of Msp1. In addition, the glycan chain appeared not to be required for the activation of Akt signaling in intestinal epithelial cells by Msp1. On the other hand, examination of different cell extracts showed that Msp1 is a glycosylated protein in the supernatant, but not in the cell wall and cytosol fraction, suggesting a link between glycosylation and secretion of this protein.

CONCLUSIONS

In this study we have provided the first evidence of protein O-glycosylation in the probiotic L rhamnosus GG. The major secreted protein Msp1 is glycosylated with ConA reactive sugars at the serine residues at 106 and 107. Glycosylation is not required for the peptidoglycan hydrolase activity of Msp1 nor for Akt activation capacity in epithelial cells, but appears to be important for its stability and protection against proteases.

Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions?

The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.

Bacterial networking

A report of the ESF-EMBO Symposium Bacterial Networks (BacNet08), Sant Feliu de Guixols, Spain, 13-18 September 2008.

A report of the ESF-EMBO Symposium Bacterial Networks (BacNet08), Sant Feliu de Guixols, Spain, 13-18 September 2008.

More robust detection of motifs in coexpressed genes by using phylogenetic information

Background

Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates.

Results

We applied our methodology to a compiled test data set containing known regulatory motifs and to two biological data sets derived from genome wide expression studies. By executing four consecutive steps of 1) identifying conserved regions in orthologous intergenic regions, 2) aligning these conserved regions, 3) clustering the conserved regions containing similar regulatory regions followed by extraction of the regulatory motifs and 4) screening the input intergenic sequences with detected regulatory motif models, our methodology proves to be a powerful tool for detecting regulatory motifs when a low signal to noise ratio is present in the input data set. Comparing our results with two other motif detection algorithms points out the robustness of our algorithm.

Conclusion

We developed an approach that can reliably identify multiple regulatory motifs lacking a high degree of overrepresentation in a set of coexpressed genes (motifs belonging to sparsely connected hubs in the regulatory network) by exploiting the advantages of using both coexpression and phylogenetic information.